code: def load_results(file_paths):
      """Concatenates the results from multiple files."""
      results = read_results(file_paths[0])
      ignore = [
          "study_name",
          "num_workers",
          "time_distro_str",
          "max_capital",
          "true_maxval",
          "num_jobs_per_worker",
          "domain_type",
          "true_argmax",
      ]
      for i in range(1, len(file_paths)):
          results_i = read_results(file_paths[i])
          diff_methods = False
          if len(results_i["methods"]) != len(results["methods"]):
              diff_methods = True
          elif not (results_i["methods"] == results["methods"]).all():
              diff_methods = True
          for key in list(results_i.keys()):
              if key in ignore:
                  continue
              elif isinstance(results_i[key], np.ndarray):
                  if results_i[key].ndim == 2:
                      if not diff_methods:
                          results[key] = np.concatenate(
                              (results[key], results_i[key]), axis=1
                          )
                      else:
                          if results[key].ndim != 2:
                              results[key] = np.expand_dims(
                                  results[key], axis=0
                              )
                          _, n1 = results_i[key].shape
                          _, n = results[key].shape
                          if n1 < n:
                              n = n1
                          results[key] = np.concatenate(
                              (
                                  results[key][:, :n],
                                  results_i[key][:, :n],
                              ),
                              axis=0,
                          )
                  elif results_i[key].ndim == 0:
                      continue
                  else:
                      results[key] = np.concatenate(
                          (results[key], results_i[key])
                      )
      return results
  
file_path: dragonfly/dragonfly/utils/plot_utils.py
imports: from __future__ import division
  import pickle
  import argparse
  import matplotlib.pyplot
  import matplotlib
  from scipy.io import loadmat
  import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: load_results(file_paths) and returns: results